Sort Items Research Articles

Pricing and Replenishment Decision System Based on LSTM

Fresh supermarket vegetables have a very short shelf life, and the quality of vegetables will also be affected by the storage time. If most of the goods are not sold on that day, they will not be sold on the next day. From the demand side, the sales volume of vegetables is related to the supply side of vegetables from April to October. At the same time, the sales space of the supermarket is limited, which makes a reasonable sales portfolio extremely important. The commercial overpricing replenishment decision system based on LSTM analyzes the relationship between daily sales volume and cost-plus pricing for each vegetable category and single product over three years. It predicts the sales volume and wholesale price of each category for the next week, determining replenishment volume and pricing strategy based on forecasted sales volume and average loss rate. The system considers limited sales space and uses the entropy method to calculate weights for key indicators like total profit. It employs the TOPSIS method to sort items, considering forecasted wholesale prices, to select items for pricing decisions. The system aims to maximize total profit while ensuring the supermarket meets minimum display conditions. By assisting supermarkets in formulating more reasonable pricing strategies, it aims to reduce overstock, backlog, or stock shortages, ultimately improving sales volume and operational efficiency.

AWAS: Auto Weigh Air Sorter Untuk Memilah Barang Berdasarkan Berat Di Bandara

Auto Weigh Air Sorter (AWAS) is a technological innovation designed to sort items based on weight at airports. This system uses Arduino as a controller and a load cell as a weight sensor. Items are sorted using a servo motor based on the data obtained from the sensor. The aim of AWAS is to improve efficiency and accuracy in baggage handling, thereby speeding up the process of pricing and baggage handling. Thus, AWAS have the potential to make a significant contribution to the aviation industry by improving service quality to passengers and reducing baggage waiting time. This research focuses on the design, implementation, and evaluation of the AWAS system, as well as its potential application in the airport context.

IOT COLOR BASED PRODUCT SORTING MACHINE

The IoT Color-Based Product Sorting Machine is a cutting-edge automated system designed to revolutionize the manufacturing and packaging industries. This innovative solution employs Internet of Things (IoT) technology to enable efficient and precise sorting of products based on their color attributes. The system integrates advanced image recognition algorithms, sensor networks, and cloud computing to streamline the sorting process. Color Based Object Sorting has a wide usage in fruit sorting as well as candy sorting industries. This system puts forward a mechanism to detect color and sort items through image processing. Once identified a mechanism is used to sort the candies into particular bins baskets. We here demonstrate this mechanism using a camera with electronic circuitry along with sorting mechanism using 3 bins. The system uses raspberry pi connected to a controller circuit to achieve this task. The controller circuit consists of a camera attached to it that detects color of a small object in front of it. A motor is used to feed an object to the camera chamber. As soon is the color is detected a signal is sent to the sorter mechanism which uses a motor to position the sorting tube towards respective section. A feeder is then used to push the object towards the tubs so that it gets sorted and next object is pulled in by the feeder. The action details are sent to the IOT server using iotgecko platform to keep track of the number of objects sorted in each section. Thus we achieve a completely automated IOT based sorting system.Color based product sorting has a wide usagein fruits sorting as well as candy sorting industries. Now a day’s industrial area requires demand for automation. Due to automation human efforts are goes on decreasing day-byday. Since last decade .This system puts forward mechanism to detect color and sort item through image processing. This mechanism is used to sort the candies into particular bins or baskets Key Words: IoT, colour sensor, servo motor, node MCU,,

Rearrangement on lattices with pick-n-swaps: Optimality structures and efficient algorithms

We study a class of rearrangement problems under a novel pick-n-swap prehensile manipulation model, in which a robotic manipulator, capable of carrying an item and making item swaps, is tasked to sort items stored in lattices of variable dimensions in a time-optimal manner. We systematically analyze the intrinsic optimality structure, which is fairly rich and intriguing, under different levels of item distinguishability (fully-labeled, where each item has a unique label, or partially-labeled, where multiple items may be of the same type) and different lattice dimensions. Focusing on the most practical setting of one and two dimensions, we develop low polynomial time cycle-following-based algorithms that optimally perform rearrangements on 1D lattices under both fully- and partially-labeled settings. On the other hand, we show that rearrangement on 2D and higher-dimensional lattices become computationally intractable to optimally solve. Despite their NP-hardness, we prove that efficient cycle-following-based algorithms remain optimal in the asymptotic sense for 2D fully- and partially-labeled settings, in expectation, using the interesting fact that random permutations induce only a small number of cycles. We further improve these algorithms to provide 1.x-optimality when the number of items is small. Simulation studies corroborate the effectiveness of our algorithms. The implementation of the algorithms from the paper can be found at github.com/arc-l/lattice-rearrangement.

Evaluating Count Prioritization Procedures for Improving Inventory Accuracy in Retail Stores

Problem definition: We compare several approaches for generating a prioritized list of items to be counted in a retail store, with the objective of detecting inventory record inaccuracy and unknown out of stocks. Academic/practical relevance: We consider both “rule-based” approaches, which sort items based on heuristic indices, and “model-based” approaches, which maintain probability distributions for the true inventory levels updated based on sales and replenishment observations. Methodology: Our study evaluates these approaches on multiple metrics using data from inventory audits we conducted at European home and personal care retailer dm-drogerie markt. Results: Our results support arguments for both rule-based and model-based approaches. We find that model-based approaches provide versatile visibility into inventory states and are useful for a broad range of objectives but that rule-based approaches are also effective as long as they are matched to the retailer’s goal. We find that “high-activity” rule-based policies, which favor items with high sales volumes, inventory levels, and past errors, are more effective at detecting inventory discrepancies. The best policies uncover over twice the discrepancies detected by random selection. A “low-activity” rule-based policy based on low recorded inventory levels, on the other hand, is more effective at detecting unknown out of stocks. The best policy detects over eight times the unknown out of stocks found by random selection. Managerial implications: Our findings provide immediate guidance to our retail partner on appropriate methods for detecting inventory record inaccuracy and unknown out of stocks. Our approach can be replicated at other retailers interested in customized optimization of their counting programs. Funding: This work was supported by the Bavarian Ministry for Science and Arts [Grant BayIntAn_KUEI_2018_43] and the EHI Foundation and GS1 Germany [Prize for Best Collaboration Between Science and Practice in Retail Research (2019)]. A. J. Mersereau thanks the Sarah Graham Kenan Foundation for support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1119 .

Why are some inhibitory tasks easy for preschool children when most are difficult? Testing two hypotheses

Understanding the processes that create inhibitory demands is central to understanding the role of inhibitory control in all aspects of development. The processes that create inhibitory demands on most developmental tasks seem clear and well understood. However, there is one inhibitory task that appears substantially easier than the others: the Reverse Categorization task, in which children are asked to “reverse sort” items (e.g., put large items in a small box and put small items in a large box). This finding is both surprising and problematic because it cannot be explained by any existing account of inhibitory development. Four experiments with 3- and 4-year-olds sought to explain why the Reverse Categorization task is easy. Two experiments (N = 64) investigated the hypothesis that children conceptualize the task in a way that reduces its inhibitory demands; and two experiments (N = 56) tested the hypothesis that the task is easier because children sort items slowly. The data indicate that children spontaneously respond more slowly on the Reverse Categorization task than on other inhibitory tasks and that this slowing reduces the task’s cognitive demands. The way in which slowed responding works, and its relation to other inhibition-reducing interventions, is discussed.

The impact of group ownership on memory

The influence of the self on memory processes has been extensively investigated (the self-reference effect) both explicitly (trait-rating paradigm) and implicitly (ownership paradigm). The groups that are closely related to the self are an important part of self-concept, and group-reference facilitated recall to the same extent as self-referencing using trait-rating paradigm. The current research employed an ownership procedure to investigate the impact of group ownership on memory using the participants’ family served as the reference group. In both experiments, participants were asked to sort items into baskets that belonged to their family or a fictitious family. A subsequent recognition test showed that there was a significant memory advantage for objects that owned by their family, and the ownership effect was found in remember, but not know, responses. This finding suggests that transient ingroup-ownership of items had a significant memory dominance effect, and the enhancing effect of ownership leads to recollective experience.

A Survey on the Visual Analytics for Data Ranking

<p indent="0mm">Ranking is a popular and universal approach to sort items based on the value of its attributes, which can make judicious and informed decisions effectively. This paper reviews the related research on the visual analysis for data ranking. Firstly, the design and application of visual elements such as coordinate axis location, length, angle, area and brightness/saturation from the perspective of visual element mapping is introduced. Secondly, with different structural forms of data for ranking, an overview of the advanced technologies and methods with respect to multidimensional, temporal, spatial and topological features is proposed. Furthermore, applications of ranking visual analysis in the human economy, urban traffic, culture, sports and entertainment are investigated. Finally, the challenges and future developments of ranking visualization are prospected.

Robotic Waste Sorting Technology: Toward a Vision-Based Categorization System for the Industrial Robotic Separation of Recyclable Waste

The use of robots in waste processing plants can significantly improve the processing of recyclables. Such robots need sophisticated visual and manipulation skills to be able to work in the extremely heterogeneous, complex, and unpredictable waste sorting industrial environment. This article considers the implementation of an autonomous robotic system for the categorization and physical sorting of recyclables according to material types. In particular, it focuses on the development of a low-cost computer vision module based on deep learning technologies to identify and sort items. To facilitate further research endeavors, the data set of recyclable images and a group of image processing scripts for object identification, masking, and synthetic placement against multiple backgrounds are available in an open source GitHub repository (https://github.com/kskmar/ReSort-IT.git). The deep-trained computer vision module is integrated with a robotic system that undertakes the physical separation of recyclables. The composite system is deployed in a waste processing plant, where it is successfully assessed in recyclable sorting under difficult and demanding industrial conditions.

PLC Operated Colour Based Product Sorting machine

Manual sorting requires excessive effort and takes time, while automated sorting makes it possible to sort items quickly and efficiently. Production and packaging systems widely use the sorting method of product lines as a bridge. Industries have been sorting processes to arrange the materials of their items according to the requirements to decrease labour and increase overall accuracy. We have developed a S71200 PLC-based sorting system to demonstrate this sorting system, where we use electro-magnetic actuators with TCS2300 colour sensor to automate the whole system. The system consists of a conveyor belt to carry items and a colour sensor to sense the product’s colour form, and the system will then use 2 electromagnetic actuators to actuate and drive the product at its respective collection station. To identify the colour of the object moving on the conveyor belt, a colour sensor was used. The colour sensor transmits a particular signal to the PLC as soon as the appropriate colour is detected, which, in turn, operates the corresponding electromagnetic actuator to force the product out of the respective station. We can completely automate the sorting system in this way. In parallel processing industries, the sorting of these goods is very difficult. A consistency issue is created by continuous manual sorting. For controlling the overall operation, PLC is used as a master controller and Arduino as a slave controller. The colour identification method is based on the frequency analysis of the colour sensor’s output. This will fulfil the requirements of higher output and accurate performance in the field of automation.

Imma Sort by Two or More Attributes With Interpretable Monotonic Multi-Attribute Sorting.

Many choice problems often involve multiple attributes which are mentally challenging, because only one attribute is neatly sorted while others could be randomly arranged. We hypothesize that perceiving approximately monotonic trends across multiple attributes is key to the overall interpretability of sorted results, because users can easily predict the attribute values of the next items. We extend a ranking principal curve model to tune monotonic trends in attributes and present Imma Sort to sort items by multiple attributes simultaneously by trading-off the monotonicity in the primary sorted attribute to increase the human predictability for other attributes. We characterize how it performs for varying attribute correlations, attribute preferences, list lengths and number of attributes. We further extend Imma Sort with ImmaAnchor and ImmaCenter to improve the learnability and efficiency to search sorted items with conflicting attributes. We demonstrate usage scenarios for two applications and evaluate its learnability, usability, interpretability, and user performance in prediction and search tasks. We find that Imma Sort improves the interpretability and satisfaction of sorting by ≥ 2 attributes. We discuss why, when, where, and how to deploy Imma Sort for real-world applications.

Textile Recognition and Sorting for Recycling at an Automated Line Using Near Infrared Spectroscopy

In order to add value to recycled textile material and to guarantee that the input material for recycling processes is of adequate quality, it is essential to be able to accurately recognise and sort items according to their material content. Therefore, there is a need for an economically viable and effective way to recognise and sort textile materials. Automated recognition and sorting lines provide a method for ensuring better quality of the fractions being recycled and thus enhance the availability of such fractions for recycling. The aim of this study was to deepen the understanding of NIR spectroscopy technology in the recognition of textile materials by studying the effects of structural fabric properties on the recognition. The identified properties of fabrics that led non-matching recognition were coating and finishing that lead different recognition of the material depending on the side facing the NIR analyser. In addition, very thin fabrics allowed NIRS to penetrate through the fabric and resulted in the non-matching recognition. Additionally, ageing was found to cause such chemical changes, especially in the spectra of cotton, that hampered the recognition.

People's sensitivity to content vs. formal properties of visual stimuli: Evidence from category construction

When people are asked to classify visual stimuli, they are often insensitive to formal properties, such as their 3D coherence or symmetry. We investigated whether this pattern of formal insensitivity would also be found using more familiar stimuli and properties: paintings that differ in their artistic style and words printed in two different typefaces. The experiments used the category formation paradigm in which subjects freely sort items into groups that seem most natural to them. They could sort each stimulus set up to three times. Only about half of the subjects in Experiment 1 ever sorted the paintings by artistic style, and only 12% did so on their first sort. Only 36% ever sorted by typeface, with many of the subjects stopping after two sorts and saying that no further categories were possible. Experiment 2 repeated the test of typeface using actual words cut out of newspapers and advertisements. Half the words were printed in boldface and half not. These items lacked any strong semantic connections, yet only 30% of subjects ever sorted the items into the bold and non-bold words. The results suggest that many people are not sensitive to the formal properties of stimuli that also have semantic content. Spontaneously noticing those differences may require a particular task with explicit instructions or experience in that domain (e.g., copyeditors or art students).

Obtaining psychological embeddings through joint kernel and metric learning

Psychological embeddings provide a powerful formalism for characterizing human-perceived similarity among members of a stimulus set. Obtaining high-quality embeddings can be costly due to algorithm design, software deployment, and participant compensation. This work aims to advance state-of-the-art embedding techniques and provide a comprehensive software package that makes obtaining high-quality psychological embeddings both easy and relatively efficient. Contributions are made on four fronts. First, the embedding procedure allows multiple trial configurations (e.g., triplets) to be used for collecting similarity judgments from participants. For example, trials can be configured to collect triplet comparisons or to sort items into groups. Second, a likelihood model is provided for three classes of similarity kernels allowing users to easily infer the parameters of their preferred model using gradient descent. Third, an active selection algorithm is provided that makes data collection more efficient by proposing comparisons that provide the strongest constraints on the embedding. Fourth, the likelihood model allows the specification of group-specific attention weight parameters. A series of experiments are included to highlight each of these contributions and their impact on converging to a high-quality embedding. Collectively, these incremental improvements provide a powerful and complete set of tools for inferring psychological embeddings. The relevant tools are available as the Python package PsiZ, which can be cloned from GitHub (https://github.com/roads/psiz).

Color Detection and Sorting Using Internet of Things Machine

Color-Based Object Sorting is widely used in fruit sorting and candy sorting industries. The system puts forward a mechanism to detect color, sort items using image processing. This mechanism uses a camera, electronic circuitry and sorting mechanism. The system uses Arduino connected to the controller circuit to achieve the task. The camera attached to Arduino and is used to detect the object’s color. The motor feeds the input to the camera section. A signal is passed to the sorter mechanism; it uses a motor and directs the sorting tube towards the respective section. The feeder feeds the next input to the camera section and the process continues until every object gets sorted. Thus, a completely automated Internet of Things (IoT) based sorting machine is achieved.

Image Similarity Searching Use Multi Part Cutting And Grayscale Color Histogram

The use of information technology in everyday life continues to increase so rapidly. This is inseparable from the role of researchers, especially in the field of information technology. Information technology has become a necessity so that it is widely used in the fields of education, trade, livestock and even to the agricultural sector. One of the obstacles that is needed is to check all activities involving information systems, especially when there is data in the form of images. Problems that arise are usually needed by humans to check and sort items that have been carried out by humans. This is the background of this research to help reduce activities involving humans. The process of finding the similarity of images in computer vision can be used in several fields such as education, retail, and other fields. In the field of computer vision education can be utilized for the automatic absence process through face recognition, in terms of retailing, it can be used for sorting through object detection. The process of finding similarities between images that are queries and dataset images will be the subject of research, and the process of calculating similarities between queries and datasets will be discussed step by step. The method used in the search process is by calculating the shortest distance between query images and dataset. The steps taken are the extraction feature and then RGB to gray color conversion. The next stage is to cut the image into four parts which will then be calculated the distance of the ecludian. The final part will calculate the performance of the algorithm using the matrix confusion method, so that the test results are in the form of error rates, precision, and accuracy. The trial process uses 30 data using 1000 datasets. In the test results obtained information in the form of recall of 1, 0.66 accuracy and 0.66precision.Keywords: Image Similarity, Histogram Image Grayscale, Ecludiance Distance.

Comparative analysis of inhibitory processes of healthy preschool children and children with the autistic spectrum disorder

Understanding the processes that create inhibitory demands is central to understanding the role of inhibitory control in all aspects of development. The processes that create inhibitory demands on most developmental tasks seem clear and well understood. However, there is one inhibitory task that appears substantially easier than the others: the Reverse Categorization task, in which children are asked to “reverse sort” items (e.g., put large items in a small box and put small items in a large box). This finding is both surprising and problematic because it cannot be explained by any existing account of inhibitory development. Four experiments with 3- and 4-year-olds sought to explain why the Reverse Categorization task is easy. Two experiments (N = 64) investigated the hypothesis that children conceptualize the task in a way that reduces its inhibitory demands; and two experiments (N = 56) tested the hypothesis that the task is easier because children sort items slowly. The data indicate that children spontaneously respond more slowly on the Reverse Categorization task than on other inhibitory tasks and that this slowing reduces the task’s cognitive demands. The way in which slowed responding works, and its relation to other inhibition-reducing interventions, is discussed.

Investigation of social supports for parents of children with Autism

There has been an increase of children being identified with autism in the United States (Center for Disease Control, 2009), leading to an increased concern of how to best meet the needs of children with autism and their families. In response to each reauthorization of the Individuals with Disabilities in Education Act (2004), in which the roles of families have been strengthened in planning their child's education and professionals have had more input, the field has tried to uncover the 'best' ways to support parents. Recommended practice suggests that parents are best able to identify their own support needs, with assistance from professionals in identifying supports to assist with these needs (Murray et al., 2007). The focus of this study was to identify the forms of social support that parents of children recently diagnosed with autism perceive as being important. Twenty parents of children recently diagnosed with autism participated in this study. These parents completed a Q-sort using the forms of social support, which allowed for a ranking from most to least important. Statistically significant correlations were found on five support items. Factor analysis was conducted to explore groups of participants with similar rankings of the Q sort items.

CONSTRUÇÃO DE INSTRUMENTO DE AVALIAÇÃO DO CONHECIMENTO SOBRE O CONTEÚDO DE UM GUIA ALIMENTAR PARA CRIANÇAS DE 2 A 3 ANOS DE IDADE

The aim of this study was to build an instrument to assess the knowledge of parents responsible for the home feeding of children (2–3 years old) on the content of a dietary guideline for children in this age group. The target population was of parents responsible for children home feeding (rchf) institutionalized in municipal daycare centers of Rio de Janeiro city. This is quali-quantitative cross-sectional study with the construction of four prototypes. Prototype 1 consisted of a database with 70 items applied to rchf of a Basic Health Unit. An inferential analysis of the statements of five focus groups (n=46 rchf), refinement techniques of items in the semantic perspective and from the view of endorsement tax allowed to create the Prototype 2 (20 items). Those items were submitted to six expert judges who were able to sort items by dimensions and refine them to define the Prototype 3 (17 items). A pretest was performed (n=60 rchf) with prototype 3. This allowed the removal of three items according to the subjective analysis of the theoretical postulates of dietary conducted by the researcher bringing the prototype 4 (14 items). This was subjected to an exploratory factor analysis, in which used KMO criterion that confirmed the possibility of factorization of matrix scores observed. The multivariate test indicated distribution of the scores of the items tending to the normal distribution. The main factor estimator showed the Kaiser-Guttman criterion, the retention of two factors (factor 1 and factor 2 = 3.7514 = 1.3963). The Scree plot graph pointed the two-dimensional, but showed the possibility of another factor, since the slope of the curve. Two simulations with 2 and 3 factors, using the estimator main factor and oblique Promax rotation presented a correlation between f1-f2 = 0.5764 for the two-dimensional model and the f1-f2 = - 0.5764; f1-f3 = 0.0040 for the three-dimensional model. The factor loadings were more parsimonious for the two-dimensional model, although with items presenting above error variance of 0.60. The Cronbach alpha coefficient for the two-dimensional model, assumed point estimate of 0.7724 (95% CI 0.7327513 - 0,805745) for dimension 1 and 0.7798 (95% CI 0.7098758 – 0.820341) for dimension 2. Even though this is an exploratory study, the scale with two factors (factor 1 with eight items and factor 2 with six items) may empirically represent the knowledge of those responsible for healthy feeding of institutionalized children of 2-3 years old. This achieves (results) may assist better nutritional practice and encourage further studies for its replication. DOI: http://dx.doi.org/10.12957/demetra.2014.14068

Performing culture at indigenous culture parks in Taiwan: Using Q method to identify the performers' subjectivities

Performance of indigenous culture at culture parks for tourism is traditionally viewed from a modernist ontological perspective as exploitative and from a managerial perspective as the provision of a service. These views might fail to accommodate the performers' subjectivities. In this Q method study the views of the performers are identified based on a sample of 30 respondents and 42 Q sort items. Respondents were performers employed at the Indigenous Peoples Culture Park in Taiwan. The replicability of a previous Q study was tested using the same design in a different research setting. In both studies two clusters of subjectivity were found: the ‘Performers’ View’ and the ‘Instructors’ View’. Neither view conforms to the modernist or managerial perspective identified in tourism research. Instead, the reflexivity of Q suggests that in the performance of indigenous culture, these fixed ontological categories are porous and situational.